Abstract
The logistic equation on population growth was proposed by Verhulst (Corresp Math Phys 10:113-126, 1838) [22], with the aim to provide a possible correction to the unrealistic exponential growth forecast by T. Malthus, (J Johnson, London, 1872) [13]. Population modeling became of particular interest in the 20\({\text {th}}\) century to biologists urged by limited means of sustenance and increasing human populations. Verhulst’s scheme was rediscovered by A. Lotka, (Elements of Mathematical Biology. Dover, New York, 1956) [12], as a simple model of a self-regulating population. Subsequently, the use of logistic dynamics spreads across a huge number of different frameworks, especially in diffusion phenomena. The logistic differential equation is a fundamental element in quantitative study of population dynamics, its use also extends to the field of epidemiology: both to describe the evolution of the infected population in deterministic models, and working in conditions of uncertainty it is the deterministic component of stochastic differential equations. This work brings a contribution to the foundational basic research on the logistic equation and its generalizations which hopefully have repercussions for epidemiologic applications.
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Acknowledgements
The model presented in Sect. 3 dates back to a collaboration started with Professor Stefano Alliney, which could not materialize due to the worsening of his health, which led to his premature death. This work is dedicated to his memory.
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Ritelli, D. (2021). Generalized Logistic Equations in Covid-Related Epidemic Models. In: Agarwal, P., Nieto, J.J., Ruzhansky, M., Torres, D.F.M. (eds) Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact. Infosys Science Foundation Series(). Springer, Singapore. https://doi.org/10.1007/978-981-16-2450-6_6
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